Nuclear Scaling ML: A Pipeline for Nuclear Segmentation and Quantitative Analysis in Large-Scale Microscopy Imaging

Nuclear Scaling ML is a modular microscopy image analysis pipeline designed specifically for processing large-scale microscopy datasets with multi-channel, multi-Z-slice, and time-series features. It integrates U-Net deep learning segmentation, ROI extraction, and quantitative analysis functions, and supports HPC high-performance computing environments. This project was developed by Tdeibert and open-sourced on GitHub (Original link: https://github.com/Tdeibert/Nuclear_Scaling_ML), released on May 29, 2026.

In modern cell biology and biomedical research, microscopy imaging technology generates massive multi-dimensional data (multi-fluorescence channels, multi-Z-axis slices, long time series). Traditional image analysis methods struggle to handle such large-scale data, and manual analysis is time-consuming, labor-intensive, and prone to subjective bias. This project aims to address this pain point by providing an end-to-end automated pipeline for nuclear segmentation, ROI extraction, and quantitative analysis, offering researchers a scalable, reproducible, and easy-to-debug solution.

Technical Architecture

1. Image Processing Module: Supports ND2 to TIFF format conversion, handles hyperstack data (channels, Z-axis, time dimensions).
2. U-Net Segmentation: Uses U-Net architecture for nuclear semantic segmentation, performing foreground/background classification, probability map generation, and binary mask creation.
3. ROI Extraction and Filtering: Automatically extracts individual nuclear ROIs, filters via size, circularity, and spatial proximity to denoise and handle overlapping nuclei.
4. Quantitative Analysis: Calculates morphological parameters such as nuclear area (square microns) and nucleus-to-cytoplasm ratio, supports dynamic change tracking for time-series data.

Design Features

• Modular: Each step is independently encapsulated for easy maintenance and testing.
• Reproducibility: Workflow driven by YAML configuration files, no need to modify source code.
• Scalability: Supports GPU acceleration, batch processing, optimized memory usage, and adapts to HPC environments.
• Debuggability: Supports Jupyter Notebook interactive analysis, but core code does not depend on the Notebook environment.

This pipeline is suitable for various biomedical research scenarios:

• Cell Cycle Research: Use time-series features to track morphological changes during nuclear division.
• Drug Screening: Batch process to evaluate the impact of compounds on nuclear size.
• Developmental Biology: Analyze changes in nuclear characteristics across different developmental stages.

Project Structure

• notebooks/: Interactive analysis and debugging
• src/: Core pipeline code
• scripts/: Batch processing and command-line workflows
• configs/: YAML configuration files
• outputs/: Generated output results (Git-ignored)

Usage

• Quickly set up the runtime environment via Conda package manager.
• HPC users can refer to the deployment guide: Transfer scripts and configurations to the cluster, run segmentation jobs, and pull back results for analysis.

Nuclear Scaling ML represents a modern biomedical image analysis paradigm, combining deep learning capabilities with domain expertise to provide researchers with powerful and easy-to-use tools. As microscopy technology advances and data volume grows, such automated and scalable analysis solutions will become increasingly important. The open-source nature of the project allows the community to participate in improvements and expand application boundaries.